Demonstration venue · read-only. Every page can be browsed; the buttons that would change it are switched off. Create an account to run TaxoReview on your own data. A simulation from public publication records: the people listed were not invited and did not agree to review.
? Tutorial for this page

Liang-Sheng Hwang

dblp:28/5332 · DBLP profile ↗
← Back
1ranked-venue papers
0as first author
0since 2021 · last 1998
—ORCID · none

Domains — the database's venue-derived domains; a paper can count in several

Artificial intelligence and machine learning · 1Systems, architecture and hardware · 1

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Artificial intelligence
1 paper
Reinforcement learning · 61% Robot navigation and mapping · 39%

Topics — the 4 heaviest of 4, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Machine learning › Reinforcement learning
exploration
0.011998
A Graph-Based Exploration Strategy of Indoor Environments by an Autonomous Mobile Robot · ICRA 1998
Machine learning › Reinforcement learning › exploration › autonomous exploration › mobile robot exploration
indoor exploration
0.011998
A Graph-Based Exploration Strategy of Indoor Environments by an Autonomous Mobile Robot · ICRA 1998
Robotics › Robot navigation and mapping
occupancy grid mapping
0.011998
A Graph-Based Exploration Strategy of Indoor Environments by an Autonomous Mobile Robot · ICRA 1998
Robotics › Robot navigation and mapping › robot mapping
topological mapping
0.011998
A Graph-Based Exploration Strategy of Indoor Environments by an Autonomous Mobile Robot · ICRA 1998

Methods — techniques the papers use, named apart from their topics

occupancy grid fusion · 0.0graph-based exploration strategy · 0.0
YearPublicationVenuePosition
1998 A Graph-Based Exploration Strategy of Indoor Environments by an Autonomous Mobile Robot
abstract
Presents a provably complete strategy for indoor environment exploration by an autonomous mobile robot. Without prior knowledge about the environment, the strategy guarantees the construction of a grid-based map, of the entire reachable area within a bounded region. Multiple map representations are utilized including a topological grid map for guiding the exploration process, a modified occupancy grid for fusing data from multiple range sensors, and a hierarchy of grids for real-time navigation. Experiments using a Nomad 200/sup TM/ robot have shown accurate map construction while navigating at a steady speed of 0.2m/sec.
Yung-Jen Hsu 0001, Liang-Sheng Hwang
ICRA2